Agentic Workflows: What They Are & How to Build the Best Ones (2026)

An agentic workflow is a coordinated sequence of AI-driven steps designed to accomplish a complex, multi-stage goal. Think of it as a pipeline where AI agents handle the decision-making at each step.

Agent vs. Workflow

Concept	Agent	Agentic Workflow
Scope	Single goal, flexible steps	Multi-stage, coordinated process
Structure	Dynamic loop	Defined pipeline with checkpoints
Error handling	Self-correcting	Stage-level retries and fallbacks
Best for	Open-ended tasks	Repeatable business processes

Anatomy of an Agentic Workflow

Input
  │
  ▼
[Stage 1: Data Gathering]  ← Agent with search tools
  │
  ▼
[Stage 2: Analysis]        ← Agent with code execution
  │
  ▼
[Stage 3: Human Review]    ← Human-in-the-loop checkpoint
  │
  ▼
[Stage 4: Output]          ← Agent formats and delivers result

Key Design Principles

1. Prefer deterministic code over LLM calls Use Python for data processing, formatting, and anything that doesn’t require reasoning. Reserve LLM calls for decisions.

2. Build in human checkpoints For high-stakes workflows, add review gates before irreversible actions (sending emails, making payments, publishing).

3. Design for failure Each stage should be independently retryable. Store intermediate results so you don’t restart from scratch.

4. Limit blast radius Scope each agent’s permissions to the minimum needed for its stage.

See Also

• Multi-Agent Pipelines — Coordinating multiple agents in parallel and sequence